Assessing Arm Volume in People During and After Treatment for Breast Cancer: Reliability and Convergent Validity of the LymphaTech System
Bibliographic record
Abstract
BACKGROUND: There are challenges related to the accurate and efficient measurement of lymphedema in people with breast cancer. The LymphaTech 3D Imaging System (LymphaTech, Atlanta, GA, USA) is a mobile, noninvasive platform that provides limb geometry measurements. OBJECTIVE: The objective of this study was to estimate the reliability and validity of the LymphaTech for measuring arm volume in the context of women seeking care in a specialty breast cancer rehabilitation clinic. DESIGN: This was a cross-sectional reliability and convergent validity study. METHODS: People who had stage I to IV breast cancer with lymphedema or were at risk for it were included. Arm volume was measured in 66 participants using the LymphaTech and perometer methods. Test-retest reliability for a single measure, limb volume difference, and agreement between methods was analyzed for 30 participants. A method-comparison analysis was also used to assess convergent validity between methods. RESULTS: Both LymphaTech and perometer methods displayed intraclass correlation coefficients (ICCs) of ≥0.99. The standard errors of measurement for the LymphaTech and length-matched perometer measurements were nearly identical. Similar intraclass correlation coefficients (0.97) and standard errors of measurement (38.0-40.7 mL) were obtained for the between-limb volume difference for both methods. The convergent validity analyses demonstrated no systematic difference between methods. LIMITATIONS: The sample size was not based on a formal sample size calculation. LymphaTech measurements included interrater variance, and perometer measurements contained intrarater variance. CONCLUSIONS: The LymphaTech had excellent test-retest reliability, and convergent validity was supported. This technology is efficient and portable and has a potential role in prospective surveillance and management of lymphedema in clinical, research, and home settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".